Search Results (6,758)

During jacking construction of prefabricated utility tunnels, asynchronous jack output and interface friction may induce internal force redistribution and deformation amplification at the leading end. Taking a triple-cell prefabricated utility tunnel in Xiong’an New Area as a case study, a three-dimensional finite element model was established considering inter-segment contact, equivalent bolted connections, and bottom-slab-bedding friction. Jack asynchrony was idealized as a quasi-static thrust imbalance, and a synchronous case, asynchronous cases with thrust differences of 5–30%, and varying friction coefficients were analyzed. For the 30% thrust-difference condition, structural responses were examined at both the gasket-compression stage and the maximum jacking-force stage. The results show that jacking loads attenuate along the tunnel length in a staged manner, with the leading end acting as the primary load-transfer zone. Increasing thrust imbalance drives the response from axial compression toward eccentric compression-bending, accompanied by monotonic increases in principal stresses and vertical displacement. Higher friction further amplifies the leading-end response; nevertheless, for the investigated configuration, stresses and deformations under a 30% thrust imbalance remain within engineeringly acceptable limits. The findings provide a basis for identifying critical leading-end locations, arranging monitoring schemes, and supporting construction control under asynchronous jacking. Full article

Background/Objectives: Missed nursing care is a recognized indicator of nursing quality and safety, while caring is a foundational concept in nursing practice. Few studies have empirically examined their relationship. This scoping review aimed to map and synthesize existing evidence on the conceptualisation, measurement approaches, and empirical relationships between caring and missed nursing care. Methods: The review was conducted using JBI methodology, reported according to PRISMA-ScR guidelines, and was registered in the Open Science Framework. Literature was searched in PubMed, CINAHL Ultimate (EBSCOhost), MEDLINE (EBSCOhost), and Web of Science, with additional grey literature searches in ProQuest Dissertations & Theses and Google Scholar. The review included studies examining caring in relation to missed nursing care across any healthcare setting. All study designs were considered. Data were extracted using an extraction tool, developed based on JBI guidelines, and piloted. Data were analyzed descriptively, tabulated, and summarized narratively. Results: Five quantitative cross-sectional studies met the inclusion criteria, conducted between 2012 and 2024 in the Philippines and Slovenia. Caring was assessed using the Caring Behaviors Inventory, Caring Ability Inventory, or CARE-Q, while missed nursing care was measured using the MISSCARE Survey or the Missed Nursing Care Scale. Most studies used Watson’s Theory of Human Caring, Duffy’s Quality Caring Model, or the Missed Nursing Care Model as theoretical frameworks. Across studies, caring behaviours and caring ability were negatively associated with missed nursing care. Conclusions: Caring can function as a moral and relational ideal and as a measurable and actionable factor related to patient outcomes. However, the evidence base remains limited, with inconsistent theoretical foundations and a lack of experimental studies. Future research should adopt theory-based, experimental approaches with diverse samples to explore causal mechanisms and evaluate strategies that strengthen caring competence and caring organizational cultures. Full article

Traditional multi-criteria decision-making methods often operate on the assumption of symmetry, presupposing that the positive impact of a criterion’s presence is perfectly complementary to the negative impact of its absence. However, in real-world decision problems, this relationship is frequently asymmetric; some criteria act merely as “delighters,” while others represent “must-have” constraints. This study proposes a novel methodology, the Presence–Absence Synthesis (PAS) Method, which addresses this asymmetry by treating the “Presence Effect” and “Absence Effect” of criteria as two independent dimensions. The method is built upon intuitionistic fuzzy sets (IFSs) to effectively model the uncertainty and hesitation inherent in expert evaluations. The applicability of the proposed approach is demonstrated through a real-world workforce management problem aimed at assigning employees to the most suitable tasks based on their competencies in a retail store. In the study, the suitability scores derived from the PAS method are integrated into a mathematical optimization model for weekly employee scheduling, presenting a two-stage decision support framework. The results and comparisons with the Technique for Order Preference by Similarity to Ideal Solution method reveal that the PAS method more effectively distinguishes critical competency gaps (i.e., criteria with high absence effects), leading to more realistic task assignments and a measurable reduction in operational risks, such as skill mismatches and infeasible schedules. Furthermore, sensitivity analysis confirms that the proposed model yields consistent and robust results under varying conditions. Beyond the retail context, the proposed PAS framework is applicable to a wide range of decision-making problems, including healthcare staff allocation, project team formation, supplier selection, and other resource allocation settings where their presence cannot compensate for the absence of critical criteria. Full article

The escalating housing crisis and the uncontrolled proliferation of informal settlements in the Global South challenge the modernist ideal of the completed architectural object. While ‘Parasitic Architecture’ is conventionally coded as an act of illegal occupation, ‘Incremental Housing’ strategies propose a controlled evolution; however, a theoretical gap exists in defining the morphological mechanics where these two concepts intersect. This study aims to bridge this gap by proposing the concept of ‘Domesticated Parasitism’. Adopting an instrumental case study model, the research analyzes the morphological evolution of the Quinta Monroy housing complex in Chile. To mitigate interpretive bias and ensure analytical objectivity, the visual reading follows a structured coding protocol that categorizes the intervention zones into three distinct layers: (1) Fixed Structural Matrix, (2) Defined Expansion Zones, and (3) User-Generated Infill. Findings from the diachronic analysis comparing the initial state with current saturation levels reveal that the host structure functions as a ‘spatial cage’ that disciplines the growth of user additions. Unlike uncontrolled urban sprawl, the visual evidence confirms that the parasitic additions strictly adhere to the vertical void geometry defined by the architect. The research concludes that the architect’s role transforms from an author of static forms to an enabler, positioning domesticated parasitism as a sustainable spatial grammar for urban densification. Full article

To address the complex multi-objective optimization problem of “cost–risk–recovery–dilution” in sublevel caving without bottom pillars under uncertainty, this study develops an operational GOOSE-based framework (OGOOSE) integrated with robust ε-constraint modeling. Methodologically, OGOOSE adopts three synergistic mechanisms: Opposition-Based Learning (OBL) for enhanced initial solution quality and spatial coverage symmetry, an Adaptive Inertia Weight (AIW) mechanism to maintain a symmetrical balance between exploration and exploitation, and a Boundary Reflection Mechanism (BRM) to ensure engineering feasibility. For modeling, an “ellipsoid-plane” geometric surrogate is employed, where the ellipsoid’s structural symmetry serves as the ideal baseline, while the Mean-CVaR criterion quantifies the asymmetry of operational risk (negative tail) under uncertainty. Taking robust cost (C) as the primary objective, the four-objective problem is decomposed via the ϵ-constraint method to enforce a balanced Pareto trade-off. Results demonstrate that OGOOSE significantly outperforms GOOSE, WOA, and HHO on CEC2017 benchmarks, achieving the lowest Friedman rank. In the engineering case study, it attains an average dilution rate of 28.95% (the lowest among comparators) without increasing unit cost or compromising recovery, demonstrating stable operational symmetry across economic and quality indicators. Sensitivity analysis of the ε-thresholds identifies an optimal “knee-point” that establishes a manageable balance between risk control (εR) and dilution limits (εP). OGOOSE effectively balances accuracy, stability, and interpretability, providing a robust tool for stabilizing complex mining systems against inherent operational asymmetry. Full article

Topological semimetals with nontrivial band structures host a variety of unconventional transport phenomena and have attracted significant attention in condensed matter physics. SnTaS₂, a recently proposed topological nodal-line superconductor with a centrosymmetric layered structure, provides an ideal platform to explore the interplay between topology and electronic transport. Here, we report a comprehensive study of the normal-state magnetotransport and magneto-thermoelectric properties of SnTaS₂ single crystals. We observed large magnetoresistance and nonlinear Hall resistivity at low temperatures, which can be well described by a two-band model, indicating the coexistence of electron and hole carriers. The Seebeck and Nernst coefficients were found to exhibit pronounced and nonmonotonic magnetic field dependences at low temperatures, consistent with multiband transport behavior. Moreover, clear quantum oscillations with a single frequency are detected in both electrical and thermoelectric measurements. Analysis of the oscillations reveals a small effective mass and a nontrivial Berry phase, suggesting that the corresponding Fermi surface arises from a topologically nontrivial band. These findings shed light on the normal-state electronic structure of SnTaS₂ and highlight the important role of topological bands in shaping its transport properties. Full article

Accurate channel estimation is critical for enabling effective directional beamforming and spectrally efficient transmission in beamspace massive multiple-input multiple-output (MIMO) systems. However, conventional model-driven algorithms are derived from idealized mathematical models and typically suffer severe performance degradation under model mismatches caused by complex and nonideal propagation environments. Although data-driven deep learning (DL) approaches can learn channel characteristics from data, they typically require large-scale training datasets and demonstrate limited generalization capability. To overcome these limitations, we propose a model-data hybrid-driven network (MD-HDN) scheme to address the wideband beamspace channel estimation problem. In the MD-HDN scheme, we unfold the vector approximate message passing (VAMP) algorithm into a trainable network, where a novel shrinkage function is introduced to enhance the estimation accuracy. Extensive numerical results confirm that the proposed MD-HDN scheme can significantly outperform existing schemes under various signal-to-noise ratio (SNR), and achieve substantial improvements in both estimation accuracy and robustness. Full article

Identifying floating residual feed is a critical technology in recirculating aquaculture systems, aiding water-quality control and the development of intelligent feeding models. However, existing research is largely based on ideal indoor environments and lacks adaptability to complex outdoor scenarios. Moreover, current methods for this task often suffer from high computational costs, poor real-time performance, and limited recognition accuracy. To address these issues, this study first validates in outdoor aquaculture tanks that instance segmentation is more suitable than individual detection for handling clustered and adhesive feed residues. We therefore propose LMFF–YOLO, a lightweight multi-scale fusion feed segmentation model based on YOLOv8n-seg. This model achieves the first collaborative optimization of lightweight architecture and segmentation accuracy specifically tailored for outdoor residual feed segmentation tasks. To enhance recognition capability, we construct a network using a Context-Fusion Diffusion Pyramid Network (CFDPN) and a novel Multi-scale Feature Fusion Module (MFFM) to improve multi-scale and contextual feature capture, supplemented by an efficient local attention mechanism at the backbone’s end for refined local feature extraction. To reduce computational costs and improve real-time performance, the original C2f module is replaced with a C2f-Reparameterization vision block, and a shared-convolution local-focus lightweight segmentation head is designed. Experimental results show that LMFF–YOLO achieves an mAP50 of 87.1% (2.6% higher than YOLOv8n-seg), enabling more precise estimation of residual feed quantity. Coupled with a 19.1% and 20.0% reduction in parameters and FLOPs, this model provides a practical solution for real-time monitoring, supporting feed waste reduction and intelligent feeding strategies. Full article

Industrial information systems based on web technologies (ISOWT) face escalating security challenges, particularly in critical sectors such as energy. Traditional qualitative security assessments often lack the ability to deliver actionable, real-time insights for managing complex, dynamic threats. This paper proposes a novel security index for evaluating ISOWT in industrial organizations by integrating fuzzy logic, metric-based evaluation, fuzzy Markov chains, and multi-agent systems. The proposed index quantifies deviations from an ideal “center of safety,” enabling early risk detection and proactive mitigation. The methodology is validated through two real-world case studies on Syria’s energy sector, namely the Ministry of Electricity website and Mahrukat fuel management system. Experimental results demonstrate substantial improvements, including a 45.9–58.5% increase in security index, 56.9–60.3% reduction in page load times, and 78.3–82.4% decrease in detected vulnerabilities. Comparative analysis shows that the proposed approach outperforms existing methods in terms of quantitative precision, real-time monitoring, and predictive capabilities. The proposed framework is scalable, automated, and adaptable, addressing key limitations of existing ISOWT security assessment techniques and providing a robust tool for enhancing system resilience. Its flexibility enable applicability across diverse industrial domains, contributing to advanced cybersecurity practices for critical infrastructure. Future work will focus on integrating advanced technologies, expanding the framework to additional sectors, developing adaptive fuzzy models, accounting for human factors, and improving visualization techniques to further address the evolving security challenges faced by industrial organizations. Full article

Ethnicity recognition has become increasingly important for a wide range of applications, highlighting the need for accurate and robust predictive models. Despite advances in machine learning, ethnicity classification remains a challenging research problem due to variations in facial features, class imbalance, and generalization issues. This study provides a concise synthesis of prior work to motivate the problem and then introduces a novel experimental framework for ethnicity recognition rather than a survey review. It proposes an improved approach that leverages transfer learning to enhance classification performance. The inclusion of various imaging sensors in the proposed methodology allows for an examination of how these imaging sensors impact the performance of facial recognition systems when a variety of images are captured under a number of real-world conditions, using professional and consumer-grade devices to create a range of conditions; from this dataset, the UTKFace dataset will be used to train and validate our method; an additional balanced dataset of Test Celebrities Faces was also created, representing five different ethnic groups (Black, Asian, White, Indian, and Other); the “Other” classification was specifically excluded for final evaluations to eliminate ambiguity and enhance stability. Rigorous preprocessing of both datasets was performed for optimal extraction of features from the sensors’ acquired images; the performance of several pre-trained CNN (Convolutional Neural Network) models (VGG16, DenseNet169, VGG19, ResNet50, MobileNetV2, InceptionV3 and EfficientNetB4) was used to identify an Ideal Hyperparameter Configuration for Optimal Performance. The resulting experimental results indicate that the VGG19 model achieved an 87% validation accuracy and a Maximum test accuracy of 75% on the Primary Dataset of Celebrity Faces; subsequently, the VGG19 model demonstrated a Range of Per-Class Accuracies, in addition to an overall accuracy of 87% across all five ethnic groups (51–90%+). This work demonstrates that leveraging transfer learning on imaging-sensor-captured images enables robust ethnicity classification with high accuracy and improved training efficiency relative to full model retraining. Furthermore, systematic hyperparameter optimization enhances model generalization and mitigates overfitting. Comparative experiments with recent state-of-the-art methods (2023–2025) further confirm that our optimized VGG19 model achieves competitive performance, reinforcing the effectiveness of the proposed reproducible and fairness-aware evaluation framework. Full article

Natural gas loss directly threatens the safety and economic viability of underground gas storage (UGS) facilities. The short-term, high-rate cyclic injection and withdrawal processes may cause fault reactivation, resulting in gas loss. Current assessment techniques are mostly concerned with overall storage performance, with few studies focusing on fault-related leakage. This study looks at a UGS facility developed from a difficult fault-block sandstone dry gas resource in China. Focusing on one of its border faults, we develop geological and numerical models to systematically examine the impacts of well-to-fault distance, gas injection rate, and gas withdrawal rate on fault leakage. The results show that numerical simulations can accurately estimate this gas loss. The well-to-fault distance, injection rate, and withdrawal rate are highlighted as critical regulating variables. There is an ideal range for the well-to-fault distance, and altering the injection/withdrawal rates of wells within this range is an effective loss mitigation approach. The crucial distance between injection–production wells and the fault in the X1 gas storage facility is 900 m. Notably, improving the gas withdrawal rate of wells close to the fault considerably minimizes leakage. Reducing the gas injection rate from 11 × 10⁴ m³/d to 7 × 10⁴ m³/d reduces natural gas loss by 353 × 10⁴ m³. Increasing the gas production rate from 9 × 10⁴ m³/d to 29 × 10⁴ m³/d reduces natural gas loss by 975 × 10⁴ m³. The findings provide a scientific basis for assessing and managing natural gas loss at boundary faults in similar UGS plants. Full article

Mean corpuscular volume ($MCV$) is a routinely measured hematological parameter that influences blood viscosity by altering red blood cell volume and packing density. Although $MCV$ is physiologically linked to hemorheological behavior, to the authors’ knowledge, its direct role in modulating large-artery hemodynamics has not been systematically quantified. This study introduces an $MCV$-driven effective Newtonian viscosity mode to evaluate the first-order impact of $MCV$ variation on carotid bifurcation flow. Rather than employing shear-dependent constitutive laws, blood viscosity was scaled through an $MCV$-based formulation, yielding three Newtonian fluids corresponding to clinically relevant $MCV$ levels of 70, 90, and 110 fL. Pulsatile CFD simulations were performed in four idealized carotid bifurcation geometries (40°, 50°, 65°, and 100°) to assess the combined influence of vascular geometry and $MCV$-dependent viscosity variation. Hemodynamic indices including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT) were quantified, and a two-way analysis of variance (ANOVA) was employed to distinguish the relative contributions of geometric configuration and $MCV$. Across the investigated $MCV$ range, increasing $MCV$ produced a geometry-dependent modulation of shear-based indices, with TAWSS increasing by up to approximately 11%, while OSI and RRT decreased by about 20–25% and 10%, respectively, particularly in geometries exhibiting pronounced flow separation. Although vascular geometry remained the dominant determinant of overall hemodynamic patterns, $MCV$-induced viscosity scaling significantly modulated low-shear and recirculation regions. These findings suggest that $MCV$-dependent viscosity scaling can complement patient-specific hemodynamic assessments and provide a rational baseline for future shear-dependent and personalized rheological modeling frameworks. Full article

Assessment of water resource spatial equilibrium (WRSE) is crucial for regional sustainable development, yet traditional methods always face difficulties in quantifying systemic differences and resolving their internal uncertainties. Accordingly, this study proposes a novel multi-attribute decision-making (MADM) model that integrates symmetric interaction entropy (SIE) with connection numbers (CNs) within a variable-weight framework. Firstly, information differences between alternatives and an ideal state were quantified by SIE, then these differences were decomposed into certain and uncertain components through the “identity–difference–opposition” (IDO) idea of CNs. In addition, a variable-weight mechanism was incorporated to enhance the model’s adaptability to regional characteristics. Applied to evaluate the WRSE in the Beijing–Tianjin–Hebei (BTH) region from 2014 to 2023, the model reveals that Hebei maintains the most favorable equilibrium state, with a partial identity potential or equal potential, followed by Beijing, while Tianjin predominantly exhibits partial opposite potential due to pronounced conflicts between its resource endowment and industrial structure. The proposed model not only enhances the sensitivity and interpretability of evaluation results but also facilitates the identification of key vulnerable indicators, thereby providing a scientific basis for formulating differentiated regional water governance strategies. Full article

This study aims to optimize the voltage profile of the grid by obtaining an optimum level of reactive power support from photovoltaic (PV) plants, thereby enhancing the efficiency of PV systems in power distribution networks and ensuring grid stability. Initially, voltage profiles in the sector, together with the structure and operating principles of PV plants, were considered in detail. Subsequently, the limits of reactive power support that can be provided by PV plants were determined. Then, the optimum levels of reactive power from the plants were determined using particle swarm optimization, genetic algorithm, Jaya algorithm, and firefly algorithm separately. The algorithms were tested through simulations conducted on a power distribution system operator in Türkiye. Additionally, a Modbus-based communication application was developed and tested, as a feasibility demonstration, to verify PV inverter accessibility and the capability of remotely writing reactive power reference setpoints. The quantitative optimization results reported in this manuscript are obtained from DIgSILENT PowerFactory simulations using the actual feeder model and time-series profiles. The results have revealed that PV plants can be effectively utilized as reactive power compensators to contribute to the operation of the grid under more ideal voltage profile conditions. In Türkiye, there is no regulatory or market mechanism to support reactive power provision from PV plants. Therefore, this study is novel in the Turkish market. The experimental results confirm that power generation from renewable energy can provide reactive support effectively when needed, which reveals that this approach is both technically feasible and practically relevant. Full article

This study investigates the mechanical performance and surface morphology of polyamide-based materials commonly used in plastic injection molding. Two resins, PA6 and PA66, were analyzed in both neat and 20 wt% glass fiber-reinforced (GF20) forms. The influence of reinforcement and material type on tensile strength and ductility was examined through integrated experimental and numerical approaches, complemented by microstructural and elemental analyses. PA6 and PA66 specimens were produced in accordance with ISO 527, and tensile tests revealed a significant increase in elastic modulus and tensile strength with glass fiber reinforcement, accompanied by a reduction in elongation at break. Flammability was evaluated via Glow Wire and Tracking tests. SEM–EDS analyses provided insights into fracture morphology and elemental distribution, showing that fiber–matrix interfacial debonding and fiber pull-out dominated failure in reinforced specimens, whereas neat polymers exhibited homogeneous surfaces. Finite element simulations performed in ANSYS Explicit Dynamics supported the experimental findings by identifying stress concentration zones and failure initiation regions. Although numerical simulations successfully captured stress distribution trends, quantitative differences were attributed to idealized modeling assumptions and processing-induced microstructural effects. Overall, this work provides a comprehensive assessment of the reinforcement effects in PA6 and PA66 systems, offering valuable guidance for material selection and design optimization in polymer-based engineering components. Full article